Inertial and displacement sensor position parameter online calibration method and device

Abstract

The application discloses an inertial and displacement sensor position parameter online calibration method and device, relates to the track detection technical field, and the method comprises the steps of: selecting the same line section, under the condition that the vehicle running speed and the axle load are kept unchanged, respectively performing forward detection and reverse detection, and collecting dynamic detection data in the two detection processes; track heights under the forward detection and the reverse detection are calculated according to the dynamic detection data; the track geometric state is kept unchanged in a certain period based on the same line section, the left side lever arm length and the right side lever arm length to be calibrated are taken as variables, the track heights are combined to construct a mean square error objective function about the left side lever arm length and the right side lever arm length; the sum of the left side lever arm length and the right side lever arm length is taken as a constraint condition, an optimization algorithm is used to solve the mean square error objective function, the optimal left side lever arm length and the right side lever arm length are obtained, and the online calibration of the position parameter is completed.

Description

Technical Field

[0001] This invention relates to the field of track detection technology, and more particularly to a method and apparatus for online calibration of position parameters of inertial and displacement sensors. Background Technology

[0002] The dynamic detection system for track geometry parameters is a key component of the modern railway's daily maintenance and operational safety assurance system. By monitoring the track's geometric parameters in real time, this system can promptly capture minute changes in the track, providing crucial data support for accurately assessing track condition and diagnosing potential defects. The data obtained not only scientifically guides maintenance operations but also plays an irreplaceable role in ensuring train operation safety and improving transportation efficiency. With the continuous expansion of railway transportation, especially the rapid development of high-speed and heavy-haul railways, higher requirements are placed on the accuracy of track geometry parameter measurement. Even millimeter-level deviations in the track can lead to safety hazards; therefore, achieving high-precision and high-reliability dynamic detection is of paramount importance.

[0003] In recent years, thanks to significant advancements in sensor technology and digital signal processing, the performance of dynamic track geometry detection systems has been continuously optimized, with substantial improvements in detection accuracy and reliability. Taking the fully digital dynamic track geometry detection system (referred to as the "digital track inspection system") as an example, this system integrates inertial measurement, linear structure optical displacement measurement, and advanced digital filtering technologies to achieve sub-millimeter level precision measurement of track geometry parameters. It has already been widely adopted by multiple railway units.

[0004] Despite significant technological breakthroughs in digital track inspection systems, their architecture, based on the collaborative measurement of inertial and displacement sensors, introduces new challenges. The accuracy of the detection data is highly dependent on the precise calibration of the position parameters between the inertial and displacement sensors. In practical engineering applications, due to limited installation space under the vehicle, on-site welding and installation of sensors, periodic disassembly and replacement, and mechanical vibration and structural aging deformation caused by long-term vehicle operation, the actual positional relationship between the sensors gradually deviates from the initial design values, thus affecting the reliability of the overall measurement results. Therefore, researching an efficient, accurate, and field-applicable online calibration method for the position parameters of inertial and displacement sensors is urgently needed.

[0005] For track inspection systems, researchers have proposed a calibration device based on the 6-DOF Stewart platform. This device simulates the complex motion of train bogies and changes in rail shape and position, continuously adjusting parameters during calibration to make the system output approximate the true value. While this method improves system calibration accuracy to some extent, it relies on expensive and complex hardware and has a cumbersome operation process, primarily limiting its application to precise joint calibration of the entire system before shipment. Furthermore, this method typically assumes fixed positional parameters between inertial and displacement sensors, failing to adequately consider potential changes during operation, thus restricting its application scenarios.

[0006] In summary, there is an urgent need for a technical solution that can overcome the above-mentioned shortcomings and perform online calibration of the position parameters of inertial and displacement sensors efficiently and accurately. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention proposes an online calibration method and apparatus for the position parameters of inertial and displacement sensors.

[0008] In a first aspect of the present invention, a method for online calibration of position parameters of an inertial and displacement sensor is proposed, comprising: Select the same section of the line, and under the condition of keeping the vehicle speed and axle load constant, conduct forward and reverse tests respectively, and collect dynamic test data during the two test processes; The track elevation is calculated based on the dynamic detection data under both forward and reverse detection conditions. Assuming the track geometry remains constant within a certain period for the same track section, the lengths of the left and right arm arms to be calibrated are used as variables. Combined with track elevation, a mean square error objective function is constructed for the lengths of the left and right arm arms. The distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left arm arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the right arm arm. Using the sum of the lengths of the left and right arms as constraints, the mean square error objective function is solved using an optimization algorithm to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

[0009] In a second aspect of the present invention, an online calibration device for the position parameters of an inertial and displacement sensor is provided, comprising: The detection module is used to select the same section of the line and, while keeping the vehicle speed and axle load constant, perform forward and reverse detection respectively, and collect dynamic detection data during the two detection processes. The track elevation calculation module is used to calculate the track elevation under forward and reverse detection based on the dynamic detection data. The mean square error objective function construction module is used to construct the mean square error objective function for the lengths of the left and right pole arms, based on the assumption that the track geometry remains unchanged within a certain period of the same track section, and taking the lengths of the left and right pole arms to be calibrated as variables, combined with the track elevation. Among them, the distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left pole arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the right pole arm. The calibration module is used to take the sum of the lengths of the left and right arms as constraints, and use an optimization algorithm to solve the mean square error objective function to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

[0010] In a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement an online calibration method for position parameters of inertial and displacement sensors.

[0011] In a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements an online calibration method for position parameters of inertial and displacement sensors.

[0012] In a fifth aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements an online calibration method for position parameters of inertial and displacement sensors.

[0013] The online calibration method and apparatus for position parameters of inertial and displacement sensors proposed in this invention can achieve online, automatic, and high-precision calibration of the arm length parameters between the inertial measurement unit and the displacement sensor without relying on external dedicated calibration equipment and manual on-site measurement. The overall solution effectively eliminates parameter deviations caused by installation and welding errors, mechanical vibration deformation, and component replacement, significantly improves the detection accuracy of geometric parameters such as track elevation, and greatly reduces maintenance costs while improving the flexibility and efficiency of calibration operations. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic flowchart of an online calibration method for position parameters of an inertial and displacement sensor according to an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram illustrating the principle of track elevation measurement according to an embodiment of the present invention.

[0017] Figure 3 This is a schematic diagram of reverse mileage increment detection according to an embodiment of the present invention.

[0018] Figure 4 This is a schematic diagram of positive mileage increase detection according to an embodiment of the present invention.

[0019] Figure 5 This is a schematic diagram of the architecture of an online calibration device for position parameters of an inertial and displacement sensor according to an embodiment of the present invention.

[0020] Figure 6 This is a schematic diagram of a computer device structure according to an embodiment of the present invention.

[0021] Figure 7 This is a schematic diagram of the conventional installation method for a track inspection system. Detailed Implementation

[0022] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0023] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0024] According to an embodiment of the present invention, an online calibration method and apparatus for position parameters of inertial and displacement sensors are proposed, relating to the field of track detection technology.

[0025] The core sensors of the track inspection system include a pulse encoder, an inertial measurement unit, and a line structured optical displacement measurement unit (also known as a laser camera assembly, hereinafter referred to as the displacement measurement unit). Figure 7As shown, for some special vehicles, where the vehicle structure does not allow for the installation of a detection beam, a common installation method is to rigidly suspend the inertial measurement unit (IMU) and displacement measurement unit (DMU) from the bottom of the vehicle body using manually welded brackets. The IMU is then fixedly connected to one of the DMUs on one side. The most significant difference between this arrangement and the method where all sensors are mounted on the detection beam is that the IMU is not centrally located. Furthermore, due to the manually welded brackets, there is a significant deviation between the positional parameters of the IMU and the two DMUs and the design values. Moreover, manually measuring these positional parameters results in a large error.

[0026] This invention addresses the problem that the actual positional relationship between sensors in track inspection systems gradually deviates from the initial design values ​​after on-site welding and installation, periodic disassembly and replacement, and long-term operation of the inspection vehicle causing mechanical vibration and structural aging deformation. It proposes an online calibration method for the position parameters of inertial and displacement sensors based on nonlinear optimization. This method establishes a mathematical model between the position parameters of the inertial and displacement sensors and the system's measured values, constructs an appropriate objective function, and uses an optimization algorithm to solve for the optimal position parameters. This achieves rapid calibration of the position parameters of the inertial and displacement sensors without relying on complex external equipment. This method not only effectively overcomes the dependence of traditional calibration processes on specialized equipment but also allows for periodic parameter correction of the system, significantly improving calibration efficiency and the overall accuracy of the track geometry inspection system. It possesses certain theoretical innovation and engineering application value.

[0027] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0028] Figure 1 This is a schematic flowchart of an online calibration method for the position parameters of an inertial and displacement sensor according to an embodiment of the present invention. Figure 1 As shown, the method includes: S101. Select the same section of the line and, while keeping the vehicle speed and axle load constant, perform forward and reverse detection respectively, and collect dynamic detection data during the two detection processes. S102, calculate the track elevation under forward and reverse detection based on the dynamic detection data; S103, assuming that the track geometry remains unchanged within a certain period of the same line section, using the lengths of the left and right arm arms to be calibrated as variables, and combining the track elevation, construct the target function of the mean square error for the lengths of the left and right arm arms; where the distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left arm arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the right arm arm; S104, using the sum of the lengths of the left and right arms as constraints, the mean square error objective function is solved using an optimization algorithm to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

[0029] To provide a clearer explanation of the above-mentioned online calibration method for the position parameters of inertial and displacement sensors, each step will be described in detail below.

[0030] In one embodiment, the method further includes: A track detection system is deployed, comprising an inertial measurement unit and displacement measurement units located on both sides of the inertial measurement unit; the inertial measurement unit is installed on the detection beam and is used to establish a dynamic measurement reference benchmark on the moving vehicle body; the displacement measurement units are used to measure the distance of the rail relative to the reference benchmark.

[0031] In one embodiment, for S101, the same line section is selected, and forward and reverse detection are performed respectively while keeping the vehicle speed and axle load constant, and dynamic detection data are collected during the two detection processes.

[0032] Specifically, the selected track sections include straight sections and curved sections. In practical applications, the actual track consists of both straight and curved sections. If parameter calibration is performed only on a single section, it cannot meet the measurement requirements of the entire track, resulting in accurate calibration for one working condition but excessive measurement errors for other working conditions. To address this, this invention selects both straight and curved sections simultaneously. This allows the optimal lever length parameters obtained from calibration to be adapted to all measurement scenarios, including straight sections with no roll and curved sections with large roll. This eliminates system measurement errors under different track shapes and ensures the accuracy and consistency of track geometry parameter detection across the entire track.

[0033] During the inspection, two dynamic inspections are performed on the same line direction but different inspection directions, namely forward inspection and reverse inspection. The route direction is either the mileage increase direction or the mileage decrease direction. The detection direction is determined based on the relationship between the vehicle's running direction and the forward direction. If the vehicle's running direction is the same as the system's forward direction, it is a forward detection; otherwise, it is a reverse detection.

[0034] The dynamic detection data includes at least: The center point elevation and vehicle roll angle are obtained based on the inertial measurement unit, and the vertical displacement is obtained based on the displacement measurement unit.

[0035] In one embodiment, for S102, the track elevation under forward detection and reverse detection is calculated based on the dynamic detection data.

[0036] The spatial curves of elevation changes of the left and right rail height measurement points with mileage are calculated using the following formula:

[0037] In the formula, , Indicates the elevation of the left and right rails; Indicates the elevation of the center point of the inertial measurement unit; , This indicates that the distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the left arm. Indicates the roll angle of the vehicle body; , This indicates the vertical displacement measured by the left displacement measurement unit and the vertical displacement measured by the right displacement measurement unit. The orbital elevation is obtained by performing a digital high-pass filter on the space curve.

[0038] Specifically, a digital high-pass filter is used to digitally filter the elevations of the left and right rail elevation measurement points to obtain the left elevation and right elevation:

[0039] In the formula, Indicates left-high-low. Indicates right-hand high / low; Indicates using right Digital filtering is performed, where z represents the unit delay operator.

[0040] In one embodiment, for S103, it is set that the track geometry remains unchanged within a certain period of time for the same line section. The lengths of the left and right pole arms to be calibrated are used as variables. Combined with the elevation difference between the left and right tracks, a mean square error objective function for the lengths of the left and right pole arms is constructed.

[0041] Assuming that the detection speed and vehicle axle load remain constant, and that there are no significant changes in the track alignment over a certain period, and that the track elevation measurements remain consistent, a mean square error objective function is constructed based on these assumptions:

[0042] In the formula, For about , The objective function for the bivariate mean square error; The number of sampling points; For positive detection, the first Left elevation at each sampling point; For reverse detection, the first The right elevation at each sampling point; For positive detection, the first The right elevation at each sampling point; For reverse detection, the first Left elevation at each sampling point; The constraints are:

[0043] In the formula, This indicates the distance between the height measurement points on the left and right rails.

[0044] In one embodiment, for S104, the sum of the lengths of the left and right arms is used as a constraint condition. The mean square error objective function is solved using an optimization algorithm to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

[0045] The formula for calculating the lengths of the left and right arm is as follows:

[0046] In the formula, For positive detection, the first Elevation of the center point of the inertial measurement unit at each sampling point; For reverse detection, the first Elevation of the center point of the inertial measurement unit at each sampling point; For reverse detection, the first Vertical displacement on the left side at each sampling point; For positive detection, the first Vertical displacement on the right side at each sampling point; For reverse detection, the first Vertical displacement on the right side at each sampling point; For positive detection, the first Vertical displacement on the left side at each sampling point; For positive detection, the first Vehicle roll angle at each sampling point; For reverse detection, the first Roll angle of the vehicle body at each sampling point.

[0047] The online calibration method proposed in this invention does not rely on expensive and complex dedicated calibration hardware and cumbersome factory calibration procedures. It can accurately solve the arm length parameters between the inertial measurement unit and the left and right side line structural optical displacement measurement units online using dynamic detection data of the same track section and the same track direction in a short period of time. It effectively solves the core problems of manual welding and installation, disassembly and replacement of sensors, long-term vibration and structural deformation of the inspection vehicle causing deviation of position parameters from the design value, and large manual measurement errors. It significantly reduces the difficulty and cost of calibration operations, and can easily realize the periodic online correction of system parameters. It significantly reduces the measurement error of track geometric parameters introduced by sensor installation position deviation, and effectively improves the measurement accuracy, long-term operational reliability and applicability to complex working conditions of the track inspection system. It is especially suitable for special track inspection vehicles without inspection beam installation conditions, and has important theoretical value and engineering promotion significance.

[0048] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0049] The online calibration method for the position parameters of the inertial and displacement sensors of the present invention will be described below with reference to specific embodiments.

[0050] refer to Figure 2 This is a schematic diagram illustrating the principle of track elevation measurement according to an embodiment of the present invention. Figure 2 As shown, the track detection system uses the inertial reference method to measure the track elevation, and the main sensors include an inertial measurement unit and a displacement measurement unit.

[0051] The inertial measurement unit is installed on the detection beam and is mainly used to establish a dynamic measurement reference benchmark on the moving vehicle body. Then, the distance of the rail relative to the reference benchmark is measured by the displacement measurement unit to obtain the spatial curve of the track elevation changing with the mileage. Finally, the track irregularity data within a certain wavelength range is obtained through a suitable high-pass filter.

[0052] exist Figure 2 middle, This refers to the elevation of the left rail. The elevation of the right rail; The elevation of the center point of the inertial measurement unit is obtained by measurement from the inertial measurement unit; The vertical displacement measured by the left displacement measurement unit; The vertical displacement measured by the right-side displacement measurement unit; The distance between the height measurement points of the left and right rails is 1511mm; This is the distance between the center of the inertial measurement unit and the elevation measurement point on the left rail (left arm). This is the distance between the center of the inertial measurement unit and the elevation measurement point on the right rail (right arm). This refers to the roll angle of the vehicle body.

[0053] The spatial curves of the elevation changes of the left and right rail height measurement points with mileage are obtained by calculating according to formulas (1) and (2). The track height is obtained after digital high-pass filtering of the spatial curves.

[0054] (1) (2) The track detection system uses equal spatial interval sampling to collect sensor data and calculate track geometric parameters. The spatial sampling interval is 0.25m, taking into account the left and right arms. , In the above-mentioned left and right rail elevation measurement model, only the vehicle body roll angle is considered. In conjunction with the actual conditions of the track, when a train passes through a level curve, due to the track superelevation, the roll angle... Significant changes will occur; the length of railway level curves typically ranges from several hundred meters to several kilometers, requiring measures to increase the roll angle. In the proportion of orbital elevation measurements, the cutoff wavelength of the high-pass filter should not be too short.

[0055] Based on the above considerations, the present invention designs a high-pass filter for detecting track elevation, the transfer function of which is shown in equation (3). This high-pass filter is implemented based on three trapezoidal window functions connected in parallel, and its cutoff wavelength is 70m.

[0056] (3)

[0057] The left elevation and low elevation measurements can be obtained by digitally filtering the elevations of the left and right elevation measurement points using the digital high-pass filter of equation (3). And right high and low The calculation relationships are shown in equations (4) and (5), where Indicates using right Perform digital filtering.

[0058] (4) (5) The track detection system defines its own positive direction based on the actual installation position of the sensors. Facing the system's positive direction, the left side is defined as the left track and the right side as the right track. The system calculates and outputs the left track elevation as the elevation of the left track under the current system's positive direction, and the right track elevation is calculated similarly. Furthermore, when the vehicle's running direction is the same as the system's positive direction, it is considered forward detection; otherwise, it is reverse detection. The track direction during the detection process can be divided into increasing mileage direction and decreasing mileage direction based on the relationship between the vehicle's running direction and the increase or decrease in track mileage during dynamic detection. Figure 3 and Figure 4 As shown, when detecting mileage increases in the forward direction and in the reverse direction on the same line, the system defines the left and right tracks exactly the same way. That is, the left track in the forward mileage increase detection corresponds to the right track in the reverse mileage increase detection, and the right track in the forward mileage increase detection corresponds to the left track in the reverse mileage increase detection.

[0059] When the inertial sensor of the track inspection system is not installed in the center, existing methods mainly rely on design drawings or manual measurement to obtain the lengths of the left and right arms. , However, the measurement results are subject to significant human error, thus affecting the accuracy of track geometry parameter measurements. To address these issues, this invention proposes a method for determining the lengths of the left and right lever arms. , The dynamic calibration method, based on multiple forward and reverse dynamic detection data, constructs a mean square error objective function and optimizes its solution. The specific process is as follows: Select two dynamic detection data points within a short period of time for the same track section (including straight and curved sections), in the same direction (increasing or decreasing mileage), but with different detection directions (forward and reverse detection). Calculate the elevation difference between the left and right tracks for each. Assume each detection data point has... Each sampling point, length of left and right arms , The solution process is as follows: (6) (7) (8) (9) In equations (6) to (9) above, the subscripts Indicates the first Each sampling point, superscript , These represent forward detection and reverse detection, respectively.

[0060] For positive detection, the first Left elevation at each sampling point; For positive detection, the first Elevation of the left rail vertex at each sampling point; For positive detection, the first Elevation of the center point of the inertial measurement unit at each sampling point; For positive detection, the first Vehicle roll angle at each sampling point; For positive detection, the first Vertical displacement on the left side at each sampling point; For positive detection, the first The right elevation at each sampling point; For positive detection, the first Elevation of the right rail vertex at each sampling point; For positive detection, the first Vertical displacement on the right side at each sampling point; For reverse detection, the first Left elevation at each sampling point; For reverse detection, the first Elevation of the left rail vertex at each sampling point; For reverse detection, the first Elevation of the center point of the inertial measurement unit at each sampling point; For reverse detection, the first Vehicle roll angle at each sampling point; For reverse detection, the first Vertical displacement on the left side at each sampling point; For reverse detection, the first The right elevation at each sampling point; For reverse detection, the first Elevation of the right rail vertex at each sampling point; For reverse detection, the first Vertical displacement on the right side at each sampling point.

[0061] Assuming the detection speed and vehicle axle load remain constant, the track alignment can be considered largely unchanged in the short term, meaning the track elevation measurements will remain consistent. Based on this assumption, a binary mean square error objective function is constructed. As shown in equation (10), the system constraints are shown in equation (11).

[0062] (10) (11) The above process is equivalent to solving the extremum problem of a multivariable function under constraints, introducing Lagrange multipliers based on the Lagrange multiplier method. Construct the Lagrange function As shown in equation (12): (12) function For variables The process of taking the partial derivatives and setting them to 0 is as follows: (13) (14) (15) Solving the system of equations simultaneously, we get: (16) (17) Based on the measured data of the same track section from the track inspection system in a short period of time, the lengths of the left and right arms can be calculated through the above steps. , .

[0063] The online calibration method for inertial and displacement sensor position parameters proposed in this invention can accurately obtain the arm parameters between the inertial measurement unit and the linear structure optical displacement measurement unit through measured data after the track inspection system equipment has been installed, finalized, or operated for a long time. This effectively reduces the measurement error introduced by uncontrollable sensor installation positions. Compared with the current method of manually measuring the arm parameters, the accuracy of the detection system is significantly improved. The research results have positive significance for further improving the applicability of digital track geometry parameter dynamic detection systems under complex operating conditions.

[0064] After introducing the method of exemplary embodiments of the present invention, the following references are made. Figure 5 An online calibration device for the position parameters of an inertial and displacement sensor according to an exemplary embodiment of the present invention will be described.

[0065] The implementation of the online calibration device for the position parameters of inertial and displacement sensors can refer to the implementation of the above method, and the repetitions will not be repeated. The term "module" or "unit" used below can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0066] Based on the same inventive concept, this invention also proposes an online calibration device for the position parameters of an inertial and displacement sensor, such as... Figure 5 As shown, the device includes: The detection module 510 is used to select the same line section and, while keeping the vehicle speed and axle load constant, perform forward and reverse detection respectively, and collect dynamic detection data during the two detection processes. The track elevation calculation module 520 is used to calculate the track elevation under forward and reverse detection based on the dynamic detection data. The mean square error objective function construction module 530 is used to construct a mean square error objective function for the lengths of the left and right arms, based on the assumption that the track geometry remains unchanged within a certain period of the same track section, using the lengths of the left and right arms to be calibrated as variables and combining the track elevation. The distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the right arm. The calibration module 540 is used to take the sum of the lengths of the left and right arms as constraints, and use an optimization algorithm to solve the mean square error objective function to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

[0067] It should be noted that although several modules of the online calibration device for the position parameters of inertial and displacement sensors have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0068] Based on the aforementioned inventive concept, such as Figure 6 As shown, the present invention also proposes a computer device 600, including a memory 610, a processor 620, and a computer program 630 stored in the memory 610 and executable on the processor 620. When the processor 620 executes the computer program 630, it implements the aforementioned online calibration method for the position parameters of inertial and displacement sensors.

[0069] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned online calibration method for position parameters of inertial and displacement sensors.

[0070] Based on the aforementioned inventive concept, this invention proposes a computer program product, which includes a computer program that, when executed by a processor, implements an online calibration method for the position parameters of inertial and displacement sensors.

[0071] The online calibration method and apparatus for position parameters of inertial and displacement sensors proposed in this invention can achieve online, automatic, and high-precision calibration of the arm length parameters between the inertial measurement unit and the displacement sensor without relying on external dedicated calibration equipment and manual on-site measurement. The overall solution effectively eliminates parameter deviations caused by installation and welding errors, mechanical vibration deformation, and component replacement, significantly improves the detection accuracy of geometric parameters such as track elevation, and greatly reduces maintenance costs while improving the flexibility and efficiency of calibration operations.

[0072] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for online calibration of position parameters of an inertial and displacement sensor, characterized in that, include: Select the same section of the line, and under the condition of keeping the vehicle speed and axle load constant, conduct forward and reverse tests respectively, and collect dynamic test data during the two test processes; The track elevation is calculated based on the dynamic detection data under both forward and reverse detection conditions. Assuming the track geometry remains constant within a certain period for the same track section, the lengths of the left and right arm arms to be calibrated are used as variables. Combined with track elevation, a mean square error objective function is constructed for the lengths of the left and right arm arms. The distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left arm arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the right arm arm. Using the sum of the lengths of the left and right arms as constraints, the mean square error objective function is solved using an optimization algorithm to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

2. The online calibration method for position parameters of an inertial and displacement sensor according to claim 1, characterized in that, The method also includes: A track detection system is deployed, comprising an inertial measurement unit and displacement measurement units located on both sides of the inertial measurement unit; the inertial measurement unit is installed on the detection beam and is used to establish a dynamic measurement reference benchmark on the moving vehicle body; the displacement measurement units are used to measure the distance of the rail relative to the reference benchmark.

3. The online calibration method for position parameters of an inertial and displacement sensor according to claim 1, characterized in that, The selected route sections include straight sections and curved sections; During the inspection, two dynamic inspections are performed on the same line direction but different inspection directions, namely forward inspection and reverse inspection. The route direction is either the mileage increase direction or the mileage decrease direction. The detection direction is determined based on the relationship between the vehicle's running direction and the forward direction. If the vehicle's running direction is the same as the system's forward direction, it is a forward detection; otherwise, it is a reverse detection.

4. The online calibration method for position parameters of an inertial and displacement sensor according to claim 1, characterized in that, The dynamic detection data includes at least: The center point elevation and vehicle roll angle are obtained based on the inertial measurement unit, and the vertical displacement is obtained based on the displacement measurement unit.

5. The online calibration method for position parameters of an inertial and displacement sensor according to claim 4, characterized in that, The orbital elevation is calculated based on the dynamic detection data under both forward and reverse detection conditions, including: The spatial curves of elevation changes of the left and right rail height measurement points with mileage are calculated using the following formula: In the formula, , Indicates the elevation of the left and right rails; Indicates the elevation of the center point of the inertial measurement unit; , This indicates that the distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the left arm. Indicates the roll angle of the vehicle body; , This indicates the vertical displacement measured by the left displacement measurement unit and the vertical displacement measured by the right displacement measurement unit. The orbital elevation is obtained by performing a digital high-pass filter on the space curve.

6. The online calibration method for position parameters of an inertial and displacement sensor according to claim 5, characterized in that, The orbital elevation is obtained by performing a digital high-pass filter on the space curve, including: The elevations of the left and right rail height measurement points are digitally filtered using a digital high-pass filter to obtain the left and right elevations / lowers: In the formula, Indicates left-high-low. Indicates right-hand high / low; Indicates using right Digital filtering is performed, where z represents the unit delay operator.

7. The online calibration method for position parameters of an inertial and displacement sensor according to claim 5, characterized in that, Assuming the track geometry remains constant within a certain period for the same track section, and using the lengths of the left and right arm poles to be calibrated as variables, combined with the elevation differences between the left and right tracks, a mean square error objective function is constructed for the lengths of the left and right arm poles, including: Assuming that the detection speed and vehicle axle load remain constant, and that there are no significant changes in the track alignment over a certain period, and that the track elevation measurements remain consistent, a mean square error objective function is constructed based on these assumptions: In the formula, For about , The objective function for the bivariate mean square error; The number of sampling points; For positive detection, the first Left elevation at each sampling point; For reverse detection, the first The right elevation at each sampling point; For positive detection, the first The right elevation at each sampling point; For reverse detection, the first Left elevation at each sampling point; The constraints are: In the formula, This indicates the distance between the height measurement points on the left and right rails.

8. The online calibration method for position parameters of an inertial and displacement sensor according to claim 7, characterized in that, Using the sum of the lengths of the left and right arms as constraints, an optimization algorithm is used to solve the mean square error objective function to obtain the optimal left and right arm lengths, thus completing the online calibration of the position parameters, including: The formula for calculating the lengths of the left and right arm is as follows: In the formula, For positive detection, the first Elevation of the center point of the inertial measurement unit at each sampling point; For reverse detection, the first Elevation of the center point of the inertial measurement unit at each sampling point; For reverse detection, the first Vertical displacement on the left side at each sampling point; For positive detection, the first Vertical displacement on the right side at each sampling point; For reverse detection, the first Vertical displacement on the right side at each sampling point; For positive detection, the first Vertical displacement on the left side at each sampling point; For positive detection, the first Vehicle roll angle at each sampling point; For reverse detection, the first Roll angle of the vehicle body at each sampling point.

9. An online calibration device for the position parameters of an inertial and displacement sensor, characterized in that, include: The detection module is used to select the same section of the line and, while keeping the vehicle speed and axle load constant, perform forward and reverse detection respectively, and collect dynamic detection data during the two detection processes. The track elevation calculation module is used to calculate the track elevation under forward and reverse detection based on the dynamic detection data. The mean square error objective function construction module is used to construct the mean square error objective function for the lengths of the left and right pole arms, based on the assumption that the track geometry remains unchanged within a certain period of the same track section, and taking the lengths of the left and right pole arms to be calibrated as variables, combined with the track elevation. Among them, the distance from the center of the inertial measurement unit to the left displacement measurement unit is the length of the left pole arm, and the distance from the center of the inertial measurement unit to the right displacement measurement unit is the length of the right pole arm. The calibration module is used to take the sum of the lengths of the left and right arms as constraints, and use an optimization algorithm to solve the mean square error objective function to obtain the optimal lengths of the left and right arms, thus completing the online calibration of the position parameters.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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